Author: cdixon

During a media tour in 2007 in which Steve Jobs showed the device to reporters, there was one instance in which a journalist criticized the iPhone’s touch-screen keyboard.

“It doesn’t work,” the reporter said.

Jobs stopped for a moment and tilted his head. The reporter said he or she kept making typos and the keys were too small for his or her thumbs.

Jobs smiled and then replied: “Your thumbs will learn.”

When the iPhone was introduced in 2007, it mystified its competitors, because it wasn’t built for the world as it existed. Wireless networks were too slow. Smartphone users only knew how to use physical keyboards. There were no software developers making apps for touchscreen phones. It frequently dropped phone calls.

But the iPhone was such a remarkable device — fans called it “The Jesus Phone” — that the world adapted to it. Carriers built more wireless capacity. Developers invented new apps and interfaces. Users learned how to rapidly type on touchscreens. Apple kept releasing better versions, fixing problems and adding new capabilities.

Smartphones are a good example of a broader historical pattern: technologies usually arrive in pairs, a strong form and a weak form. Here are some examples:

Strong

Weak

Public internet

Private intranets

Consumer web

Interactive TV

Crowdsourced encyclopedia (Wikipedia)

Expert-curated encyclopedia (e.g. Nupedia, Encarta)

Crowdsourced video (YouTube)

Video tech for media companies (e.g. RealPlayer)

Internet video chat (Skype)

Voice-over-IP (e.g. Vonage)

Streaming music (Spotify)

MP3 downloads (e.g. iTunes)

Touchscreen smartphones with full operating system and app store (iPhone)

Limited-app smartphones with physical keyboards (e.g. Blackberry)

Fully electric cars (Tesla)

Hybrid cars

Permissionless blockchains powered by cryptocurrencies

Permissioned/private blockchains

Public cloud

Private / hybrid cloud

App-based media companies (e.g. Netflix)

Video on demand delivered by cable companies

Virtual realty

Augmented reality

E-sports

Traditional sports delivered over the internet

Strong technologies capture the imaginations of technology enthusiasts. That is why many important technologies start out as weekend hobbies. Enthusiasts vote with their time, and, unlike most of the business world, have long-term horizons. They build from first principles, making full use of the available resources to design technologies as they ought to exist. Sometimes these enthusiasts run large companies, in which case they are often, like Steve Jobs, founders who have the gravitas and vision to make big, long-term bets.

The mainstream technology world notices the excitement and wants to join in, but isn’t willing to go all the way and embrace the strong technology. To them, the strong technology appears to be some combination of strange, toy-like, unserious, expensive, and sometimes even dangerous. So they embrace the weak form, a compromised version that seems more familiar, productive, serious, and safe.

Strong technologies often develop according to the Perez/Gartner hype cycle:

During the trough of disillusionment, entrepreneurs and others who invested in strong technologies sometimes lose faith and switch their focus to weak technologies, because the weak technologies appear nearer to mainstream adoption. This is usually a mistake.

That said, weak forms of technology can be successful. For example, it is very likely that augmented reality will be important, watching traditional sports on the internet will be popular, and so on.

But it’s strong technologies that end up defining new eras. What George Bernard Shaw said about people also applies to technologies:

The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man.

Weak technologies adapt to the world as it currently exists. Strong technologies adapt the world to themselves. Progress depends on strong technologies. Your thumbs will learn.

“How to hit home runs: I swing as hard as I can, and I try to swing right through the ball… The harder you grip the bat, the more you can swing it through the ball, and the farther the ball will go. I swing big, with everything I’ve got. I hit big or I miss big.” – Babe Ruth

One of the hardest concepts to internalize for those new to VC is what is known as the “Babe Ruth effect”:

Building a portfolio that can deliver superior performance requires that you evaluate each investment using expected value analysis. What is striking is that the leading thinkers across varied fields — including horse betting, casino gambling, and investing — all emphasize the same point. We call it the Babe Ruth effect: even though Ruth struck out a lot, he was one of baseball’s greatest hitters. — ”The Babe Ruth Effect: Frequency vs Magnitude” [pdf]

The Babe Ruth effect occurs in many categories of investing, but is especially pronounced in VC. As Peter Thiel observes:

Actual [venture capital] returns are incredibly skewed. The more a VC understands this skew pattern, the better the VC. Bad VCs tend to think the dashed line is flat, i.e. that all companies are created equal, and some just fail, spin wheels, or grow. In reality you get a power law distribution.

The Babe Ruth effect is hard to internalize because people are generally predisposed to avoid losses. Behavioral economists have famously demonstrated that people feel a lot worse about losses of a given size than they feel good about gains of the same size. Losing money feels bad, even if it is part of an investment strategy that succeeds in aggregate.

People usually cite anecdotal cases when discussing this topic, because it’s difficult to get access to comprehensive VC performance data. Horsley Bridge, a highly respected investor (Limited Partner) in many VC funds, was kind enough to share with me aggregated, anonymous historical data on the distribution of investment returns across the hundreds of VC funds they’ve invested in since 1985.

As expected, the returns are highly concentrated: about ~6% of investments representing 4.5% of dollars invested generated ~60% of the total returns. Let’s dig into the data a little more to see what separates good VC funds from bad VC funds.

Home runs: As expected, successful funds have more “home run” investments (defined as investments that return >10x):

(For all the charts shown, the X-axis is the performance of the VC funds: great VC funds are on the right and bad funds are on the left.)

Great funds not only have more home runs, they have home runs of greater magnitude. Here’s a chart that looks at the average performance of the “home run” (>10x) investments:

The home runs for good funds are around 20x, but the home runs for great funds are almost 70x. As Bill Gurley says: “Venture capital is not even a home run business. It’s a grand slam business.”

Strikeouts: The Y-axis on the this chart is the percentage of investments that lose money:This is the same chart with the Y-axis weighted by dollars invested per investment:

As expected, lots of investments lose money. Venture capital is a risky business.

Notice that the curves are U-shaped. It isn’t surprising that the bad funds lose money a lot, or that the good funds lose money less often than the bad funds. What is interesting and perhaps surprising is that the great funds lose money more often than good funds do. The best VCs funds truly do exemplify the Babe Ruth effect: they swing hard, and either hit big or miss big. You can’t have grand slams without a lot of strikeouts.

The core growthprocess in the technology business is a mutually reinforcing, multi-step, positive feedback loop between platforms and applications. This leads to exponential growth curves (Peter Thiel calls them power law curves), which in idealized form look like:

The most prominent recent example of this was the positive feedback loop between smartphones (iOS and Android phones) and smartphone apps (FB, WhatsApp, etc):

After the fact, exponential curves look relatively smooth. When you are in the midst of them, however, they feel like they are divided into two stages: gradual and sudden.

Singularity University calls this the “deception of linear vs exponential growth”:

Today, smartphone growth seems obviously exponential. But just a few years ago many people thought smartphones were growing linearly. Even Mark Zuckerberg underestimated the importance of mobile in the “feels gradual” phase. In 2011 or so, he realized what we were experiencing was actually an exponential curve, and consequently dramatically increased Facebook’s investment in mobile:

Exponential growth curves in the “feels gradual” phase are deceptive. There are many things happening today in technology that feel gradual and disappointing but will soon feel sudden and amazing.

Over the past decade, computing resources that were previously available only to large organizations became available to almost anyone. Using cloud-scale development platforms like Amazon Web Services, developers can write software that runs on hundreds or even thousands of servers, and do so relatively cheaply.

But it is still difficult to write software that makes efficient use of this abundant computing. For some projects, like creating websites, there are well-known software architectures that work reasonably well. In other areas, there’s been progress building generalized tools (for example, Hadoop in data processing). For the most part, however, developers need to solve the parallelization problem over and over again for each application they develop. New tools that help them do this are sorely needed.

Today, I am excited to announce that a16z is investing $20M in Improbable, a London-based company that was founded by a group of computer scientists from the University of Cambridge. Improbable’s technology solves the parallelization problem for an important class of problems: anything that can be defined as a set of entities that interact in space. This basically means any problem where you want to build a simulated world. Developers who use Improbable can write code as if it will run on only one machine (using whatever simulation software they prefer, including popular gaming/physics engines like Unity and Unreal), without having to think about parallelization. Improbable automatically distributes their code across hundreds or even thousands of machines, which then work together to create a seamlessly integrated, simulated world.

The Improbable team had to solve multiple hard problems to make this work. Think of their tech as a “spatial operating system”: for every object in the world — a person, a car, a microbe —the system assigns “ownership” of different parts of that entity to various worker programs. As entities move around (according to whatever controls them — code, humans, real-world sensors) they interact with other entities. Often these interactions happen across machines, so Improbable needs to handle inter-machine messaging. Sometimes entities need to be reassigned to new hardware to load balance. When hardware fails or network conditions degrade, Improbable automatically reassigns the workload and adjusts the network flow. Getting the system to work at scale under real-world conditions is a very hard problem that took the Improbable team years of R&D.

One initial application for the Improbable technology is in gaming. Game developers have been trying to build virtual worlds for decades, but until now those worlds have been relatively small, usually running on only a handful of servers and relying on hacks to create the illusion of scale. With Improbable, developers can now create games with millions of persistent, complex, interacting entities. In addition, they can spend their time inventing game features instead of building back-end systems.

Beyond gaming, Improbable is useful in any field that models complex systems — biology, economics, defense, urban planning, transportation, disease prevention, etc. Think of simulations as the flip side to “big data.” Data science is useful when you already have large data sets. Simulations are useful when you know how parts of the system work and want to generate data about the system as a whole. Simulations are especially well suited for asking hypothetical questions: what would happen to the world if we changed X and Y? How could we change X and Y to get the outcome we want?

Improbable was started three years ago at Cambridge by Herman Narula and Rob Whitehead. They have since built an outstanding team of engineers and computer scientists from companies like Google and top UK computer science programs. They’ve done all of this on a small seed financing, supplemented by customer revenue and research grants. We are thrilled to partner with Improbable on their mission to develop and popularize simulated worlds.

I felt it the first time when I visited a school. It was third and fourth graders, and they had a whole classroom full of Apple II’s. I spent a few hours there, and I saw these third and fourth graders growing up completely different than I grew up because of this machine.

What hit me about it was that here was this machine that very few people designed — about four in the case of the Apple II — who gave it to some other people who didn’t know how to design it but knew how to make it, to manufacture it. They could make a whole bunch of them. And then they give it some people that didn’t know how to design it or manufacture it, but they knew how to distribute it. And then they gave it to some people that didn’t knew how to design or manufacture or distribute it, but knew how to write software for it.

Gradually this sort of inverse pyramid grew. It finally got into the hands of a lot of people — and it all blossomed out of this tiny little seed.

It seemed like an incredible amount of leverage. It all started with just an idea. Here was this idea, taken through all of these stages, resulting in a classroom full of kids growing up with some insights and fundamentally different experiences which, I thought, might be very beneficial to their lives. Because of this germ of an idea a few years ago.

That’s an incredible feeling to know that you had something to do with it, and to know it can be done, to know that you can plant something in the world and it will grow, and change the world, ever so slightly.